accuracy¶

paddle.static. accuracy ( input, label, k=1, correct=None, total=None ) [source]

accuracy layer. Refer to the https://en.wikipedia.org/wiki/Precision_and_recall This function computes the accuracy using the input and label. If the correct label occurs in top k predictions, then correct will increment by one.

Note

the dtype of accuracy is determined by input. the input and label dtype can be different.

Parameters

• input (Tensor) – The input of accuracy layer, which is the predictions of network. A Tensor with type float32,float64. The shape is [sample_number, class_dim] .

• label (Tensor) – The label of dataset. Tensor with type int32,int64. The shape is [sample_number, 1] .

• k (int, optional) – The top k predictions for each class will be checked. Data type is int64 or int32. Default is 1.

• correct (Tensor, optional) – The correct predictions count. A Tensor with type int64 or int32. Default is None.

• total (Tensor, optional) – The total entries count. A tensor with type int64 or int32. Default is None.

Returns

Tensor, The correct rate. A Tensor with type float32.

Examples

import numpy as np
import paddle
import paddle.static as static
import paddle.nn.functional as F
paddle.enable_static()
data = static.data(name="input", shape=[-1, 32, 32], dtype="float32")
label = static.data(name="label", shape=[-1,1], dtype="int")
fc_out = static.nn.fc(x=data, size=10)
predict = F.softmax(x=fc_out)
result = static.accuracy(input=predict, label=label, k=5)
place = paddle.CPUPlace()
exe = static.Executor(place)
exe.run(static.default_startup_program())
x = np.random.rand(3, 32, 32).astype("float32")
y = np.array([[1],[0],[1]])
output = exe.run(feed={"input": x,"label": y},
                 fetch_list=[result])
print(output)
# [array(0.33333334, dtype=float32)]